PREFERENCE MODEL FOR THAI LEASING COMPANIES

In Thailand, the numbers of commercial and non commercial banks have increased which include leasing companies dramatically. Competition in the banking markets is severe. In this paper we concentrate only in Chinagmai province. So the purpose of this paper is to explore which factors are important for customer preference to use service.In this paper, 64 factors are categorized by market mixed (7P) Questionnaires are developed for survey. Neural network, a data mining tool, is used to develop framework for preference. For developing model, C# Programming is used. K fold validation is used to validation this model (90:10). The result of training model is 95%. The first model is called a preference model. It is used to predict if customer selects commercial banks, non commercial banks or leasing companies. When option is made, model would give information on the reasons. The result of prediction is 81%. The benefit of this model is to address the strategy to any provider satisfy customer preference.

 

Keywords: Preference model, Commercial and Non Commercial banks, Leasing companies, Neural Network.

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